Interest in commercial use of unmanned aerial vehicles (UAVs) for delivering products to customers is growing. In 2013, well-known companies have demonstrated or experimented with UAVs for use as autonomous delivery vehicles. Others have proposed using UAVs for delivering medical supplies and other critical goods in developing countries that lack transportation infrastructure.
These commercial demonstrations of UAVs have relied on GPS navigation systems for guidance. A weakness of this technology is that GPS signals do not reach all delivery locations. Such GPS “dead zones” are typically located near buildings in urban settings where many deliveries are likely to occur.
Lane departure warnings systems are among the driver-assistance features included in late-model automobiles. Prior-art systems use vision-based localization, which is both inefficient and intermittently reliable. They require capturing images using millions of image sensor pixels and computationally demanding image processing to extract lane locations. These image-based systems depend on clear views of lane markings unobstructed by, for example, rain, ice, and fog.
Seeking higher fuel efficiency and increased payload capacity, commercial airlines have investigated wing morphing, which involves dynamically deforming an aircraft's wing shape in response to in-flight conditions. Techniques for measuring wing deformation, such as deflection and torsion, have included post-processing of both monoscopic and stereoscopic images. These systems are computationally inefficient and sensitive to environmental factors such as clouds and rain, which may blur images and hence result in inaccurate measurements. The systems are also bulky if high-resolution cameras are required—especially in stereographic systems that require two cameras.